Trust in Science in the 2018 Global Health Monitor Report

library(tidyverse)
library(gt)
use("glue", "glue")
use("readxl", "read_xlsx")
use("htmltools", "HTML")
use("here", "here")
use("htmlwidgets", "onRender")
use("RColorBrewer", "brewer.pal")
use("scales", c("label_percent", "percent", "alpha"))

df <- read_xlsx(
  here(
    "wgm2018-dataset-crosstabs-all-countries.xlsx"
  ),
  sheet = 2
)

df <- "0=Not assigned, 1=Africa,2=Africa,3=Africa,4=Africa,5=Africa,6=Americas,7=Americas,8=Americas,9=Asia,10=Asia,11=Asia,12=Asia,13=Middle East,14=Former Soviet Union,15=Europe,16=Europe,17=Europe,18=Asia" |>
  str_split(",") |>
  unlist() |>
  str_trim() |>
  as_tibble() |>
  separate_wider_delim(value, "=", names = c("Regions_Report", "Region")) |>
  mutate(Regions_Report = as.numeric(Regions_Report)) |>
  right_join(df) |>
  select(-Regions_Report)

df <- "1=United States, 2=Egypt, 3=Morocco, 4=Lebanon, 5=Saudi Arabia, 6=Jordan, 8=Turkey, 9=Pakistan, 10=Indonesia, 11=Bangladesh, 12=United Kingdom, 13=France, 14=Germany, 15=Netherlands, 16=Belgium, 17=Spain, 18=Italy, 19=Poland, 20=Hungary, 21=Czech Republic, 22=Romania, 23=Sweden, 24=Greece, 25=Denmark, 26=Iran, 28=Singapore, 29=Japan, 30=China, 31=India, 32=Venezuela, 33=Brazil, 34=Mexico, 35=Nigeria, 36=Kenya, 37=Tanzania, 38=Israel, 39=Palestinian Territories, 40=Ghana, 41=Uganda, 42=Benin, 43=Madagascar, 44=Malawi, 45=South Africa, 46=Canada, 47=Australia, 48=Philippines, 49=Sri Lanka, 50=Vietnam, 51=Thailand, 52=Cambodia, 53=Laos, 54=Myanmar, 55=New Zealand, 57=Botswana, 60=Ethiopia, 61=Mali, 62=Mauritania, 63=Mozambique, 64=Niger, 65=Rwanda, 66=Senegal, 67=Zambia, 68=South Korea, 69=Taiwan, 70=Afghanistan, 71=Belarus, 72=Georgia, 73=Kazakhstan, 74=Kyrgyzstan, 75=Moldova, 76=Russia, 77=Ukraine, 78=Burkina Faso, 79=Cameroon, 80=Sierra Leone, 81=Zimbabwe, 82=Costa Rica, 83=Albania, 84=Algeria, 87=Argentina, 88=Armenia, 89=Austria, 90=Azerbaijan, 96=Bolivia, 97=Bosnia and Herzegovina, 99=Bulgaria, 100=Burundi, 103=Chad, 104=Chile, 105=Colombia, 106=Comoros, 108=Republic of Congo, 109=Croatia, 111=Cyprus, 114=Dominican Republic, 115=Ecuador, 116=El Salvador, 119=Estonia, 121=Finland, 122=Gabon, 124=Guatemala, 125=Guinea, 128=Haiti, 129=Honduras, 130=Iceland, 131=Iraq, 132=Ireland, 134=Ivory Coast, 137=Kuwait, 138=Latvia, 140=Liberia, 141=Libya, 143=Lithuania, 144=Luxembourg, 145=Macedonia, 146=Malaysia, 148=Malta, 150=Mauritius, 153=Mongolia, 154=Montenegro, 155=Namibia, 157=Nepal, 158=Nicaragua, 160=Norway, 163=Panama, 164=Paraguay, 165=Peru, 166=Portugal, 173=Serbia, 175=Slovakia, 176=Slovenia, 183=Eswatini, 184=Switzerland, 185=Tajikistan, 186=The Gambia, 187=Togo, 190=Tunisia, 191=Turkmenistan, 193=United Arab Emirates, 194=Uruguay, 195=Uzbekistan, 197=Yemen, 198=Kosovo, 202=Northern Cyprus" |>
  str_split(",") |>
  unlist() |>
  str_trim() |>
  as_tibble() |>
  separate_wider_delim(value, "=", names = c("WP5", "Country")) |>
  mutate(WP5 = as.numeric(WP5)) |>
  right_join(df) |>
  select(-WP5)

region_colours <- brewer.pal(7, "Dark2")
library(leaflet)
library(rnaturalearth)
library(sf)
Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.4.0; sf_use_s2() is TRUE
science <- df |>
  drop_na(Q1) |>
  mutate(science = Q1 < 3, across(c(Country, Region), fct)) |>
  group_by(Country, Region) |>
  summarize(percent = mean(science), .groups = "drop")

country_data <- ne_countries(scale = "medium", returnclass = "sf") |>
  mutate(
    admin = case_when(
      admin == "United States of America" ~ "United States",
      admin == "Czechia" ~ "Czech Republic",
      admin == "Palestine" ~ "Palestinian Territories",
      admin == "Republic of the Congo" ~ "Republic of Congo",
      admin == "Gambia" ~ "The Gambia",
      admin == "United Republic of Tanzania" ~ "Tanzania",
      admin == "North Macedonia" ~ "Macedonia",
      admin == "Republic of Serbia" ~ "Serbia",
      admin == "Republic of Serbia" ~ "Serbia",
      TRUE ~ admin
    )
  ) |>
  right_join(science, by = join_by(admin == Country)) |>
  drop_na(scalerank)
vaccine <- df |>
  drop_na(Q25, Q26) |>
  mutate(
    safe = Q25 %in% c(4, 5),
    effective = Q26 %in% c(4, 5),
    across(c(Country, Region), fct)
  ) |>
  group_by(Country, Region) |>
  summarize(
    safe = mean(safe),
    effective = mean(effective),
    .groups = "drop"
  )

Science

Column - 1

Perceived Science Knowledge per Country

Perceived Science Knowledge per Country

Percentage of people who answered “a lot” or “some” to the question: How much do you, personally, know about science? Do you know a lot, some, not much, or nothing at all?

country_colours <- colorNumeric(
  palette = "Purples",
  domain = country_data$percent,
  na.color = "#bdbdbd"
)

country_labels <- glue(
  "<strong>{country_data$admin}</strong><br/>{percent(country_data$percent, accuracy = 0.1)}"
) |>
  lapply(HTML)

country_data |>
  leaflet() |>
  addTiles(options = tileOptions(noWrap = TRUE)) |>
  addPolygons(
    fillColor = ~ country_colours(percent),
    weight = 1,
    opacity = 1,
    color = "white",
    dashArray = "3",
    fillOpacity = 0.7,
    label = country_labels,
    highlightOptions = highlightOptions(
      weight = 3,
      color = "#666",
      dashArray = "",
      fillOpacity = 0.9,
      bringToFront = TRUE
    ),
    group = "Countries"
  ) |> 
  onRender("
    function(el, x) {
      // Select the map container element (el) and set its background style
      el.style.backgroundColor = 'transparent';
    }
  ") 

Data

science |>
  arrange(desc(percent)) |>
  gt() |>
  fmt_percent(columns = percent, decimals = 1) |>
  cols_label(
    Country = "Country",
    percent = "Science Knowledge"
  ) |>
  opt_row_striping(FALSE) |>
  data_color(
    columns = Region,
    method = "factor",
    palette = alpha(region_colours, 0.5)
  )
Country Region Science Knowledge
United States Americas 83.4%
Norway Europe 81.7%
Turkmenistan Asia 79.7%
Denmark Europe 76.8%
France Europe 75.2%
Luxembourg Europe 74.8%
Northern Cyprus Not assigned 73.7%
Canada Americas 73.2%
Sweden Europe 73.0%
Armenia Asia 72.6%
Spain Europe 72.3%
Belarus Former Soviet Union 72.0%
Germany Europe 72.0%
Switzerland Europe 71.2%
Slovenia Europe 70.6%
Belgium Europe 69.7%
Ukraine Former Soviet Union 69.3%
United Kingdom Europe 68.1%
Finland Europe 67.3%
Poland Former Soviet Union 66.6%
Azerbaijan Asia 66.4%
Portugal Europe 65.5%
Iran Middle East 65.3%
Australia Asia 64.6%
Kenya Africa 64.5%
Montenegro Europe 64.4%
Hungary Former Soviet Union 63.2%
Estonia Europe 62.9%
New Zealand Asia 62.6%
United Arab Emirates Middle East 62.5%
Moldova Former Soviet Union 62.3%
Sri Lanka Asia 62.2%
Turkey Middle East 61.5%
Singapore Asia 61.4%
Netherlands Europe 61.3%
Lebanon Middle East 61.1%
Iceland Europe 61.0%
Malta Europe 61.0%
Cyprus Europe 60.8%
Croatia Europe 60.3%
Ireland Europe 59.9%
Cameroon Africa 59.3%
Mauritius Africa 58.6%
Saudi Arabia Middle East 58.5%
Russia Former Soviet Union 58.4%
Palestinian Territories Middle East 58.0%
Uganda Africa 57.6%
Austria Europe 57.6%
Jordan Middle East 57.6%
Ivory Coast Africa 57.4%
Chad Africa 57.0%
Serbia Europe 57.0%
Georgia Asia 56.9%
Tajikistan Asia 56.1%
Philippines Asia 55.6%
Iraq Middle East 55.3%
Benin Africa 55.1%
Kuwait Middle East 54.6%
Lithuania Europe 54.4%
Bosnia and Herzegovina Europe 54.3%
Algeria Africa 54.2%
Kazakhstan Asia 53.4%
Gabon Africa 53.1%
Ghana Africa 52.7%
Nepal Asia 52.2%
Kyrgyzstan Asia 51.8%
Peru Americas 51.0%
Romania Former Soviet Union 50.9%
Macedonia Europe 50.9%
Dominican Republic Americas 50.8%
Uzbekistan Asia 50.7%
Libya Africa 50.6%
Republic of Congo Africa 50.5%
Malaysia Asia 50.5%
Haiti Americas 50.2%
Zambia Africa 49.7%
Slovakia Former Soviet Union 49.7%
Bolivia Americas 49.1%
Namibia Africa 48.7%
Tunisia Africa 48.7%
Bangladesh Asia 47.6%
South Korea Asia 47.5%
Guinea Africa 47.3%
Tanzania Africa 47.2%
Burkina Faso Africa 47.2%
Italy Europe 47.1%
Kosovo Not assigned 47.1%
India Asia 47.0%
Zimbabwe Africa 46.9%
Czech Republic Former Soviet Union 46.7%
Taiwan Asia 45.6%
Ethiopia Africa 45.1%
Israel Middle East 45.0%
Panama Americas 44.8%
Latvia Europe 44.7%
Nicaragua Americas 44.3%
Togo Africa 44.3%
Morocco Africa 43.7%
Bulgaria Former Soviet Union 43.7%
Argentina Americas 43.4%
Mali Africa 42.8%
Uruguay Americas 42.8%
Afghanistan Asia 42.3%
Costa Rica Americas 42.1%
Venezuela Americas 41.7%
Ecuador Americas 41.7%
Liberia Africa 41.6%
Mexico Americas 41.3%
Nigeria Africa 41.1%
Chile Americas 41.0%
Mauritania Africa 40.7%
Egypt Africa 40.3%
Botswana Africa 39.5%
Comoros Africa 39.4%
Senegal Africa 39.3%
Mongolia Asia 39.3%
Greece Europe 39.0%
Burundi Africa 38.4%
Colombia Americas 38.3%
Mozambique Africa 38.2%
Pakistan Asia 37.8%
Albania Europe 37.2%
Indonesia Asia 36.9%
Paraguay Americas 36.4%
Thailand Asia 36.2%
Japan Asia 35.8%
El Salvador Americas 35.5%
Honduras Americas 34.9%
Yemen Middle East 34.8%
Guatemala Americas 33.9%
Madagascar Africa 33.5%
South Africa Africa 33.1%
Eswatini Africa 31.5%
Laos Asia 30.2%
Myanmar Asia 29.8%
Cambodia Asia 28.2%
Brazil Americas 28.0%
Niger Africa 27.2%
The Gambia Africa 23.8%
Sierra Leone Africa 23.1%
Vietnam Asia 22.7%
Malawi Africa 22.5%
China Asia 21.6%
Rwanda Africa 12.0%

Column - 2

Americans had the highest percentage of respondents who perceived themselves as being knowledgeable about science (~83%); however, around 10% of respondents disagreed on the safety of vaccines and about 5% of respondents disputed vaccines’ effectiveness.

Surveyed respondents from Egypt expressed some of the least disbelief in the effectiveness (~0.5%) and safety (~0.3%) amongst all respondents. This stands in stark contrast to the United States.

Vaccine Belief

Column - 1

Vaccine Safety and Efficacy

library(plotly)

Attaching package: 'plotly'
The following object is masked from 'package:ggplot2':

    last_plot
The following object is masked from 'package:stats':

    filter
The following object is masked from 'package:graphics':

    layout
p <- vaccine |>
  ggplot(aes(
    x = safe,
    y = effective
  )) +
  geom_point(aes(
    text = glue(
      "Country: {Country}<br>",
      "Region: {Region}<br>",
      "Safe: {percent(safe, accuracy = 0.01)}<br>",
      "Effective: {percent(effective, accuracy = 0.01)}"
    ),
    color = Region
  )) +
  geom_smooth(method = "lm", se = FALSE, linewidth = 0.5) +
  labs(
    x = "Disagree on Safety",
    y = "Disagree on Efficacy",
    title = "Positive Relation Between Disbelief in Vaccine Safety and Efficacy"
  ) +
  scale_x_continuous(labels = label_percent()) +
  scale_y_continuous(labels = label_percent()) +
  scale_color_manual(values = region_colours) +
  theme_bw() +
  theme(legend.position = "none")
Warning in geom_point(aes(text = glue("Country: {Country}<br>", "Region:
{Region}<br>", : Ignoring unknown aesthetics: text
ggplotly(p, tooltip = "text")
`geom_smooth()` using formula = 'y ~ x'
vaccine |>
  mutate(tmp = (safe + effective) / 2) |> 
  arrange(tmp) |>
  select(-tmp) |> 
  gt() |>
  fmt_percent(columns = c(safe, effective), decimals = 1) |>
  cols_label(
    safe = "Safety",
    effective = "Efficacy"
  ) |>
  data_color(
    columns = Region,
    method = "factor",
    palette = alpha(region_colours, 0.5)
  )
Country Region Safety Efficacy
Egypt Africa 0.3% 0.5%
Bangladesh Asia 0.8% 0.4%
Tajikistan Asia 1.3% 1.0%
Rwanda Africa 1.8% 0.8%
Laos Asia 2.1% 0.9%
India Asia 2.1% 1.9%
Hungary Former Soviet Union 2.5% 1.5%
Lebanon Middle East 3.2% 1.2%
Ethiopia Africa 3.1% 1.5%
Malawi Africa 2.8% 1.8%
Palestinian Territories Middle East 2.7% 1.9%
Uzbekistan Asia 3.5% 1.3%
Singapore Asia 2.8% 2.2%
Thailand Asia 2.8% 2.3%
Tanzania Africa 1.8% 3.5%
Northern Cyprus Not assigned 4.1% 1.6%
Jordan Middle East 3.2% 2.5%
Myanmar Asia 3.7% 2.3%
Zimbabwe Africa 4.1% 1.9%
Venezuela Americas 3.8% 2.2%
Dominican Republic Americas 4.0% 2.3%
Israel Middle East 4.7% 1.7%
Afghanistan Asia 4.5% 1.9%
Nicaragua Americas 2.9% 3.6%
Greece Europe 4.1% 2.5%
Kuwait Middle East 4.0% 2.6%
Cambodia Asia 5.3% 1.8%
Yemen Middle East 3.5% 3.6%
Morocco Africa 3.3% 4.1%
Malaysia Asia 2.9% 4.6%
Slovakia Former Soviet Union 5.5% 2.3%
Tunisia Africa 4.7% 3.1%
Argentina Americas 4.7% 3.3%
Saudi Arabia Middle East 4.3% 3.8%
Iraq Middle East 4.5% 3.6%
United Arab Emirates Middle East 4.5% 3.7%
Ghana Africa 3.9% 4.6%
Sierra Leone Africa 4.2% 4.6%
Czech Republic Former Soviet Union 6.1% 2.7%
Burundi Africa 5.9% 3.1%
Norway Europe 6.2% 2.8%
Turkmenistan Asia 4.2% 4.8%
South Korea Asia 6.9% 2.2%
Australia Asia 6.0% 3.3%
Pakistan Asia 4.7% 4.9%
Costa Rica Americas 5.1% 4.6%
Turkey Middle East 6.5% 3.4%
Sri Lanka Asia 7.1% 2.9%
Spain Europe 5.5% 4.7%
The Gambia Africa 5.1% 5.1%
Poland Former Soviet Union 6.0% 4.3%
Iran Middle East 8.3% 2.0%
Panama Americas 5.0% 5.5%
United Kingdom Europe 8.1% 2.6%
China Asia 7.9% 2.9%
Honduras Americas 5.8% 5.1%
Nepal Asia 4.0% 6.9%
Japan Asia 8.2% 2.9%
Comoros Africa 8.4% 2.7%
Kosovo Not assigned 5.8% 5.4%
Niger Africa 7.3% 4.0%
Ireland Europe 8.0% 3.5%
Malta Europe 6.8% 5.0%
Lithuania Europe 7.9% 3.9%
Libya Africa 7.1% 4.7%
Finland Europe 7.7% 4.1%
Croatia Europe 7.4% 4.5%
Kenya Africa 4.7% 7.4%
Mexico Americas 6.8% 5.4%
Vietnam Asia 9.1% 3.2%
Ecuador Americas 5.8% 6.5%
Cyprus Europe 7.8% 4.7%
Serbia Europe 8.1% 4.5%
Portugal Europe 4.9% 8.4%
Botswana Africa 9.2% 4.1%
Denmark Europe 9.0% 4.4%
Sweden Europe 10.9% 2.5%
Bosnia and Herzegovina Europe 7.9% 5.9%
Georgia Asia 9.2% 4.6%
Guatemala Americas 7.6% 6.7%
Paraguay Americas 7.0% 7.4%
Canada Americas 8.4% 6.3%
Mozambique Africa 5.3% 9.6%
Mauritania Africa 10.3% 4.7%
United States Americas 10.2% 5.0%
New Zealand Asia 9.3% 6.1%
Brazil Americas 9.3% 6.6%
Romania Former Soviet Union 10.9% 5.2%
Madagascar Africa 10.7% 5.7%
Uruguay Americas 9.7% 6.8%
Kazakhstan Asia 11.1% 5.8%
Kyrgyzstan Asia 9.4% 7.9%
Colombia Americas 10.0% 7.3%
Indonesia Asia 5.8% 11.7%
Eswatini Africa 10.4% 7.2%
Chile Americas 11.5% 6.7%
Germany Europe 14.4% 3.8%
Zambia Africa 8.6% 9.7%
Algeria Africa 10.7% 7.8%
Italy Europe 13.6% 4.9%
Estonia Europe 12.8% 6.3%
Azerbaijan Asia 10.8% 8.3%
Luxembourg Europe 13.7% 5.6%
Moldova Former Soviet Union 11.3% 8.1%
El Salvador Americas 11.5% 7.9%
Slovenia Europe 11.8% 7.8%
Bolivia Americas 12.3% 7.8%
South Africa Africa 8.7% 11.3%
Chad Africa 11.5% 8.7%
Macedonia Europe 12.5% 7.7%
Montenegro Europe 13.1% 7.2%
Mongolia Asia 15.1% 5.4%
Mauritius Africa 16.4% 4.9%
Mali Africa 16.2% 5.5%
Uganda Africa 8.7% 12.9%
Republic of Congo Africa 18.4% 3.9%
Guinea Africa 15.4% 7.3%
Iceland Europe 21.3% 1.4%
Nigeria Africa 6.1% 16.7%
Benin Africa 15.0% 7.8%
Bulgaria Former Soviet Union 15.4% 7.5%
Ukraine Former Soviet Union 15.9% 7.8%
Albania Europe 15.4% 8.4%
Senegal Africa 14.3% 9.6%
Philippines Asia 14.2% 9.8%
Cameroon Africa 15.3% 9.0%
Belarus Former Soviet Union 15.4% 10.0%
Taiwan Asia 19.0% 6.9%
Ivory Coast Africa 18.1% 8.0%
Namibia Africa 11.0% 15.9%
Latvia Europe 17.0% 10.0%
Belgium Europe 21.5% 5.6%
Burkina Faso Africa 21.2% 9.0%
Netherlands Europe 18.4% 12.1%
Haiti Americas 21.2% 9.5%
Liberia Africa 2.3% 28.8%
Switzerland Europe 22.5% 9.1%
Austria Europe 21.0% 11.2%
Peru Americas 17.6% 15.2%
Armenia Asia 20.8% 13.1%
Togo Africa 24.8% 10.2%
Russia Former Soviet Union 23.4% 12.2%
Gabon Africa 26.9% 11.3%
France Europe 31.8% 15.9%

Column - 2

Vaccine Belief by Region and Country

Percentage of people who agreed with the statement: Vaccines are safe.

library(gghalves)
# Taken from https://github.com/teunbrand/ggplot_tricks?tab=readme-ov-file#lets-begin
my_fill <- aes(fill = after_scale(alpha(colour, 0.3)))
# A small nudge offset
offset <- 0.025

vaccine_info <- df |>
  drop_na(Q25) |>
  mutate(
    vaccine = Q25 < 3,
    across(c(Country, Region), fct)
  ) |>
  filter(Region != "Not assigned") |>
  group_by(Region, Country) |>
  summarize(percent = mean(vaccine), .groups = "drop_last")

vaccine_info |>
  ggplot(
    aes(
      x = fct_reorder(Region, percent),
      y = percent,
      colour = Region,
      !!!my_fill
    )
  ) +
  geom_half_violin(side = "l", trim = FALSE, scale = "width") +
  geom_half_boxplot(
    side = "l",
    coef = 0,
    width = 0.4,
    outliers = FALSE,
    alpha = 0.3
  ) +
  geom_half_dotplot(
    method = "histodot",
    stackdir = "up",
    dotsize = 1,
    binwidth = 0.01
  ) +
  scale_y_continuous(labels = label_percent(), limits = c(0, 1)) +
  scale_color_manual(values = region_colours) +
  scale_fill_manual(values = region_colours) +
  coord_flip() +
  labs(
    x = element_blank(),
    y = element_blank(),
    title = element_blank()
  ) +
  theme_bw() +
  theme(legend.position = "none")
Warning: Removed 278 rows containing missing values or values outside the scale range
(`geom_half_violin()`).

vaccine_info |>
  ungroup() |>
  mutate(Region = fct_reorder(Region, percent, .desc = TRUE)) |>
  group_by(Region) |>
  arrange(desc(percent), .by_group = TRUE) |>
  gt() |>
  fmt_percent(columns = percent, decimals = 1) |>
  cols_label(
    percent = "Vaccine Belief"
  ) |>
  data_color(
    columns = Region,
    method = "factor",
    palette = alpha(region_colours, 0.5)
  )
Country Vaccine Belief
Asia
Bangladesh 97.7%
India 95.8%
Afghanistan 94.2%
Thailand 93.1%
Uzbekistan 92.3%
Myanmar 92.1%
Laos 92.1%
Tajikistan 91.1%
Nepal 90.9%
Malaysia 90.2%
Cambodia 89.8%
Turkmenistan 87.4%
Pakistan 86.7%
Sri Lanka 86.6%
Australia 85.2%
Indonesia 84.4%
Philippines 78.0%
Singapore 75.3%
Mongolia 74.8%
New Zealand 74.6%
China 74.1%
Vietnam 73.0%
Kyrgyzstan 71.2%
Georgia 68.6%
Azerbaijan 63.5%
Taiwan 53.9%
South Korea 51.8%
Kazakhstan 44.2%
Armenia 43.6%
Japan 34.4%
Middle East
Palestinian Territories 92.1%
Jordan 90.8%
Iraq 89.7%
Kuwait 86.9%
Lebanon 85.0%
Yemen 84.7%
United Arab Emirates 83.5%
Saudi Arabia 82.4%
Turkey 79.6%
Israel 75.6%
Iran 68.0%
Americas
Nicaragua 93.5%
Venezuela 92.3%
Dominican Republic 90.8%
Mexico 88.5%
Honduras 88.3%
Argentina 87.1%
Costa Rica 87.1%
Panama 86.4%
Guatemala 86.3%
Ecuador 84.7%
Colombia 81.7%
Brazil 81.1%
El Salvador 77.4%
Paraguay 77.2%
Canada 76.6%
United States 75.8%
Bolivia 75.2%
Chile 75.1%
Uruguay 74.0%
Haiti 70.1%
Peru 69.8%
Africa
Egypt 97.1%
Liberia 96.5%
Ethiopia 95.7%
Tanzania 95.6%
Rwanda 94.2%
Sierra Leone 92.9%
Malawi 92.8%
Kenya 90.8%
Mozambique 89.9%
Nigeria 89.2%
Burundi 88.5%
Comoros 87.9%
The Gambia 87.3%
Ghana 86.4%
Zimbabwe 85.9%
Niger 85.2%
Madagascar 84.4%
Uganda 84.3%
Namibia 83.9%
Zambia 81.7%
Chad 81.2%
South Africa 81.1%
Botswana 80.8%
Benin 80.6%
Morocco 78.9%
Tunisia 78.3%
Guinea 78.3%
Libya 78.2%
Ivory Coast 75.9%
Eswatini 74.9%
Republic of Congo 73.8%
Mauritania 73.7%
Senegal 73.0%
Cameroon 72.4%
Mali 69.7%
Burkina Faso 67.9%
Algeria 64.9%
Mauritius 61.0%
Gabon 60.8%
Togo 53.8%
Europe
Spain 82.5%
Norway 82.1%
Portugal 80.5%
Malta 78.3%
United Kingdom 77.4%
Greece 76.9%
Ireland 76.9%
Finland 76.7%
Italy 75.1%
Sweden 72.6%
Slovenia 71.6%
Bosnia and Herzegovina 70.9%
Denmark 70.5%
Luxembourg 69.3%
Serbia 67.8%
Netherlands 67.3%
Germany 66.7%
Cyprus 64.1%
Iceland 62.4%
Albania 61.5%
Belgium 60.9%
Croatia 58.0%
Austria 55.8%
Macedonia 55.2%
Lithuania 53.5%
Switzerland 51.9%
France 50.9%
Estonia 48.6%
Montenegro 44.6%
Latvia 44.2%
Former Soviet Union
Hungary 78.9%
Poland 76.4%
Slovakia 74.2%
Czech Republic 65.2%
Romania 64.8%
Russia 43.7%
Moldova 43.0%
Bulgaria 39.4%
Belarus 37.8%
Ukraine 30.2%